Search Results (252)

Large-sized components with numerous small key local features are essential in advanced manufacturing. Achieving high-precision quality control necessitates accurate and highly efficient three-dimensional (3D) measurement techniques. A flexible measurement system integrating a fringe-projection-based 3D scanner with an industrial robot is developed to enable the rapid measurement of large object surfaces. To enhance overall measurement accuracy, we propose an enhanced calibration method utilizing a multidimensional ball-based calibrator to simultaneously calibrate for hand-eye transformation and robot kinematic parameters. Firstly, a preliminary hand-eye calibration method is introduced to compensate for measurement errors at observation points, leveraging geometric-constraint-based optimization and a virtual single point derived via the barycentric calculation method. Subsequently, a distance-constrained calibration method is proposed to jointly estimate the hand-eye transformation and robot kinematic parameters, wherein a distance error model is constructed to link parameter errors with the measured deviations of a virtual single point. Finally, calibration and validation experiments were carried out, and the results indicate that the maximum and average measurement errors were reduced from 1.053 mm and 0.814 mm to 0.421 mm and 0.373 mm, respectively, thereby confirming the effectiveness of the proposed method. Full article

This paper examines the integration of markerless augmented reality (AR) within the da Vinci Surgical Robot, utilizing artificial intelligence (AI) for improved precision. The main challenge in creating AR for these systems is the small size (5 mm diameter) of the cameras used. Traditional camera-calibration approaches produce significant errors when used for miniature cameras. Further, the use of external markers can be obstructive and inaccurate in dynamic surgical environments. The study focuses on overcoming these limitations of traditional AR methods by employing advanced neural networks for camera calibration and real-time image processing. We demonstrate the use of a dense neural network to reduce the total projection error by directly learning the mapping of a 3D point to a 2D image plane. The results show a median error of 7 pixels (1.4 mm) when using a neural network, as compared to an error of 50 pixels (10 mm) when using a more traditional approach involving camera calibration and robot kinematics. This approach not only enhances the accuracy of AR for surgical procedures but also offers a more seamless integration with existing robotic platforms. These research findings underscore the potential of AI in revolutionizing AR applications in medical robotics and other teleoperated systems, promising efficient and safer interventions. Full article

With the rise of vehicle automation, accurate estimation of driving dynamics has become crucial for ensuring safe and efficient operation. Vehicle dynamics control systems rely on these estimates to provide necessary control variables for stabilizing vehicles in various scenarios. Traditional model-based methods use wheel-related measurements, such as steering angle or wheel speed, as inputs. However, under low-traction conditions, e.g., on icy surfaces, these measurements often fail to deliver trustworthy information about the vehicle states. In such critical situations, precise estimation is essential for effective system intervention. This work introduces an AI-based approach that leverages perception sensor data, specifically camera images and lidar point clouds. By using relative kinematic relationships, it bypasses the complexities of vehicle and tire dynamics and enables robust estimation across all scenarios. Optical and scene flow are extracted from the sensor data and processed by a recurrent neural network to infer vehicle states. The proposed method is vehicle-agnostic, allowing trained models to be deployed across different platforms without additional calibration. Experimental results based on real-world data demonstrate that the AI-based estimator presented in this work achieves accurate and robust results under various conditions. Particularly in low-friction scenarios, it significantly outperforms conventional model-based approaches. Full article

Adobe churches are representative of Andean architectural heritage, yet their structural vulnerability to seismic events remains a significant concern. This study evaluates the seismic performance of the 17th-century Church of Santo Tomás de Aquino in Rondocan, Peru, an adobe building that underwent conservation work in the late 1990s. The assessment combines in situ inspections and experimental testing with advanced nonlinear numerical modeling. A finite-element macro-model was developed and calibrated using sonic and ambient vibration tests to replicate the observed structural behavior. Nonlinear static (pushover) analyses were performed in the four principal directions to identify failure mechanisms and to evaluate seismic capacity using the Peruvian seismic code. Kinematic limit analyses were conducted to assess out-of-plane mechanisms using force- and displacement-based criteria. The results revealed critical vulnerabilities in the rear façade and lateral walls, particularly in terms of out-of-plane collapse, while the main façade exhibited a higher capacity but a brittle failure mode. This study illustrates the value of advanced numerical simulations, calibrated with field data, as effective tools for assessing seismic vulnerability in historic adobe buildings. The outcomes highlight the necessity of strengthening measures to balance life safety requirements with preservation goals. Full article

This study proposes a systematic solution to the motion planning challenges in dual-robot collaborative grinding and polishing systems, with its effectiveness experimentally validated. By establishing a dual-robot pose constraint model, this study innovatively integrates the “handshake” method with the seven-point calibration approach, achieving enhanced spatial mapping accuracy between the base coordinate system and tool coordinate system. Based on the modified Denavit–Hartenberg (DH) method, this study establishes kinematic modeling for EPSON C4-A901S robots on the MATLAB platform. By integrating calibration parameters, a dual-robot collaborative grinding model is constructed, with its reliability thoroughly verified through comprehensive simulations. An experimental platform integrating dual EPSON C4-series robots with grinding devices, clamping fixtures, and drive systems was established. The average error below 8 mm from 10 repeated experiments fully validates the accuracy and practical applicability of the integrated calibration method. Full article

The patch loading resistance of slender steel plate girders is a critical factor in the design of launched steel and composite steel–concrete bridges. Traditional design methods enhance patch loading resistance through various stiffening techniques, with contributions typically estimated via code expressions calibrated on experimental data that do not always reflect the complexities of full-scale bridge applications. Finite Element (FE) modeling offers a more realistic alternative, though its practical application is often hindered by modeling uncertainties and nonlinearities. To bridge this gap, this paper introduces an advanced FE modeling approach. It provides a comprehensive description of an FE model that accurately predicts both the load–displacement behavior and the patch loading resistance. The model is benchmarked against a broad set of experimental tests and systematically investigates the effects of key modeling parameters and their interactions—material stress–strain law, boundary condition representation, stiffness of the load introduction area, initial geometric imperfections, and solving algorithms. Key findings demonstrate that a bilinear elastoplastic material model with hardening is sufficient for estimating ultimate resistance, and kinematic constraints can effectively replace rigid transverse stiffeners. The stiffness of the load application zone significantly influences the response, especially in launched bridge scenarios. Initial imperfections notably affect both stiffness and strength, with standard fabrication tolerances offering suitable input values. The modified Riks algorithm is recommended for its efficiency and stability in nonlinear regimens. The proposed methodology advances the state of practice by providing a simple yet reliable FE modeling approach for predicting patch loading resistance in real-world bridge applications, leading to safer and more reliable structural designs. Full article

In our contribution, we conduct a thematic literature review on trajectory estimation using a decentralized multi-sensor system based on robotic total stations (RTS) with a focus on unmanned aerial system (UAS) platforms. While RTS are commonly used for trajectory estimation in areas where GNSS is not sufficiently accurate or is unavailable, they are rarely used for UAS trajectory estimation. Extending the RTS with integrated camera images allows for UAS pose estimation (position and orientation). We review existing research on the entire RTS measurement processes, including time synchronization, atmospheric refraction, prism interaction, and RTS-based image evaluation. Additionally, we focus on integrated trajectory estimation using UAS onboard measurements such as IMU and laser scanning data. Although many existing articles address individual steps of the decentralized multi-sensor system, we demonstrate that a combination of existing works related to UAS trajectory estimation and RTS calibration is needed to allow for trajectory estimation at sub-$\mathrm{c}$$\mathrm{m}$ and sub-$0.01$ gon accuracies, and we identify the challenges that must be addressed. Investigations into the use of RTS for kinematic tasks must be extended to realistic distances (approx. 300–500 m) and speeds (>2.5 m s⁻¹). In particular, image acquisition with the integrated camera must be extended by a time synchronization approach. As to the estimation of UAS orientation based on RTS camera images, the results of initial simulation studies must be validated by field tests, and existing approaches for integrated trajectory estimation must be adapted to optimally integrate RTS data. Full article

This study presents a bilateral control architecture that links fluoroscopic image feedback directly to the kinematics of a tendon-driven, three-joint robotic catheter and a 3-DoF motorised C-arm, intending to preserve optimal imaging geometry during autonomous catheter insertion and thereby mitigating radiation exposure. Forward and inverse kinematics for both manipulators were derived via screw theory and geometric analysis, while a calibrated projection model generated synthetic X-ray images whose catheter bending angles were extracted through intensity thresholding, segmentation, skeletonisation, and least-squares circle fitting. The estimated angle fed a one-dimensional extremum-seeking routine that rotated the C-arm about its third axis until the apparent bending angle peaked, signalling an orthogonal view of the catheter’s bending plane. Implemented in a physics-based simulator, the framework achieved inverse-kinematic errors below 0.20% for target angles between ${20}^{°}$ and ${90}^{°}$, with accuracy decreasing to 3.00% at ${10}^{°}$. The image-based angle estimator maintained a root-mean-square error $\le 3$% across most of the same range, rising to 6.4% at ${10}^{°}$. The C-arm search consistently located the optimal perspective, and the combined controller steered the catheter tip along a predefined aortic path without collision. These results demonstrate sub-degree angular accuracy under idealised, noise-free conditions and validate real-time coupling of image guidance to dual-manipulator motion; forthcoming work will introduce realistic image noise, refined catheter mechanics, and hardware-in-the-loop testing to confirm radiation-dose and workflow benefits. Full article

Accurate kinematic calibration of subreflector actuators is essential for pointing precision of large radio telescopes, particularly at high frequencies. Conventional least-squares methods are vulnerable to noise and outliers, and their accuracy may degrade when limited pose diversity leads to poor parameter excitation. To address these challenges, this paper proposes a novel robust self-calibration framework that integrates Huber loss and $L_2$ regularization into the Levenberg–Marquardt (LM) algorithm—yielding a hybrid optimization approach that combines residual robustness, numerical stability, and convergence reliability. A comprehensive simulation study was conducted under varying workspace sizes and sensor noise levels. The proposed method maintained stable performance even under reduced excitation and high-noise conditions, where traditional LM methods typically degrade, confirming its robustness and applicability to realistic calibration scenarios. The framework was further validated using a structured-light 6-DOF pose measurement system, the proposed method achieved over 90% improvement in both position and orientation accuracy compared to the traditional LM approach. These findings confirm the method’s effectiveness for high-precision 6-DOF calibration in parallel mechanisms, and its suitability for real-world applications in radio telescope subreflector alignment. Full article

Pile foundations are commonly used to support structures subjected to complex loading conditions. In seismic-prone regions, understanding the soil–pile interaction under cyclic loading is essential for ensuring the stability and safety of these foundations. Numerical modeling is an effective tool for predicting the nonlinear behavior of soil under seismic excitation, but selecting an appropriate constitutive model remains a significant challenge. This study investigates the seismic behavior of pile groups embedded in soft clay using advanced finite element analysis. The piles are modeled as aluminum with a linear elastic response and are analyzed within a soil domain characterized by two kinematic hardening constitutive models based on the Von Mises failure criterion. Model parameters are calibrated using a combination of experimental and numerical data. The study also examines the influence of pile spacing within the group on seismic response, revealing notable differences in the response patterns. The results show that the nonlinear kinematic hardening model provides a more accurate correlation with experimental centrifuge test results compared to the multilinear model. These findings contribute to enhancing the understanding of soil–pile interaction under seismic loading and improving the design of pile foundations. Full article

This paper proposes an analytical framework for pre-analyzing the potential performance of online sensor calibration in Kalman filtering. Taking a multi-sensor integrated kinematic positioning and navigation system as an example, a pre-analysis of the system performance can be conducted: the observability of individual sensor systematic error states; minimum estimable values of sensor systematic error states; and minimum detectable systematic errors in sensor observations. These measures together allow for a rigorous characterization of the potential performance of a system as part of mission planning. The proposed framework enables a thorough evaluation of the relative value of different calibration maneuvers and sensor configurations before data collection by simulating the anticipated trajectory, without even requiring the construction of a physical system. When used with the Generic Multisensor Integration Strategy (GMIS), the proposed framework provides unique insight into the potential performance of IMU sensors. To illustrate the utility of the proposed framework, two situations were analyzed: one where no specific calibration maneuvers were undertaken and one where a circular motion maneuver was undertaken. The results show the potential and practicality of the proposed framework in firmly establishing best practices for field procedures and learning about the system’s capability when using online sensor calibration. Full article

This study presents a systematic calibration of discrete element model (DEM) parameters for fermented grains (19.4% moisture) using the Hertz–Mindlin with JKR contact model to optimize robotic end-effector design in liquor distillation. By integrating cylinder lift experiments and response surface methodology, we identified three critical parameters (JKR surface energy, restitution, and rolling friction coefficients) through Plackett–Burman screening and steepest ascent optimization. The Box–Behnken design derived optimal values of 0.0429 J/m² surface energy, 0.183 restitution coefficient, and 0.216 rolling friction coefficient. The validation results demonstrated excellent agreement between the simulated and experimental angles of repose (AORs), with a simulated AOR of 36.805° and an experimental value of 36.412°, corresponding to a 1.08% error. The geometric congruence of the deposition morphology and the notable symmetrical distribution characteristics of the left and right angles of repose confirm the robustness of the parameter calibration methodology. This study provides a theoretical basis for the kinematic parameter optimization of the end-effector distribution mechanism in fermented grain-loading robots, providing critical insights for advancing automated control in solid-state liquor distillation processes. Full article

The advancement of inertial measurement unit (IMU) technology has opened new opportunities for motion analysis, yet its widespread adoption in clinical practice remains constrained by the high costs of proprietary systems, lengthy setup procedures, and the need for specialized expertise. To address these challenges, we present a multi-IMU system designed with streamlined calibration, efficient data processing, and a focus on accessibility for patient-facing applications. Although initially developed for human gait analysis, the modular design of this system enables adaptability across diverse motion tracking scenarios. This work outlines the system’s technical framework, including protocols for data acquisition, derivation of gait variables, and considerations for user-friendly software deployment. We further illustrate its utility by measuring lower-limb gait kinematics in near-real time and providing stride-to-stride biofeedback using a single sensor. These initial results underscore the potential of this system for both laboratory-based gait assessment and rehabilitation interventions in clinical environments and future work will assess validation against traditional optical motion capture methods. Full article

This paper presents an innovative method for estimating vehicle sideslip angle by integrating a dynamic–kinematic coupled Unscented Kalman Filter (UKF) with an adaptive strategy that ensures accuracy across various surface conditions and operational scenarios. This research employs a two-degree-of-freedom vehicle kinematic model for state updates and constructs a vehicle dynamic model, utilizing parameters obtained from real vehicle calibration to monitor the system. Additionally, this paper thoroughly explores the performance characteristics and applicable conditions of both dynamic and kinematic models. It proposes reference speed factors, surface friction factors, and lateral characteristic factors to indicate the confidence levels of the two models under different operating conditions and address state estimation requirements across diverse scenarios. Thence, the adaptive strategy proactively adjusts the noise covariance matrix to achieve an optimal balance between the dynamic and kinematic models. The effectiveness of the adaptive UKF estimation strategy is validated through real vehicle tests conducted under various scenarios with differing friction coefficients and operational conditions. The results indicate that the proposed strategy surpasses existing approaches utilizing the Luenberger observer and UKF observer in all scenarios. Notably, on low-friction surfaces and during extreme maneuvers, the experimental results underscore the superior performance facilitated by the adaptive strategy. Full article

Steel structures subjected to earthquakes or extreme cyclic loadings may undergo extensive damage and fractures due to ultra-low-cycle fatigue (ULCF). Although assessments of damage initiation and evolution parameters have been carried out for some steels exposed to low-cycle fatigue, so far, these parameters for structural steels exposed to ULCF have neither been sufficiently studied nor quantified. Accordingly, this paper provides the results of finite element analysis (FEA) concerning the ULCF behaviors of S355 and S690 steel specimens. Calibration of the damage parameters is performed in SIMULIA Abaqus 6.11 FEA software using a direct cyclic algorithm and available experimental data. Kliman’s model for the hysteresis energy of cyclic loading is used to analytically verify the damage parameters. In addition, available experimental data were obtained from cyclic axial strain tests on S355 and S690 steel specimens according to the ASTM International standard E606/E606M-21. Finally, the non-linear Chaboche–Lemaitre (C–L) combined isotropic–kinematic hardening model is used for the characterization of the ULCF behavior of S355 steel in a simple cylindrical bar. It is found that the two damage initiation parameters are 5.0 and −0.8, the first damage initiation parameter is dominant when modeling the number of cycles to failure, and the second damage initiation parameter is a material constant. Full article